Selective sampling magnetic resonance-based method for assessing structural spatial frequencies

ABSTRACT

The disclosed embodiments provide a method for acquiring MR data at resolutions down to tens of microns for application in in-vivo diagnosis and monitoring of pathology for which changes in fine tissue textures can be used as markers of disease onset and progression. Bone diseases, tumors, neurologic diseases, and diseases involving fibrotic growth and/or destruction are all target pathologies. Further the technique can be used in any biologic or physical system for which very high-resolution characterization of fine scale morphology is needed. The method provides rapid acquisition of selected values in k-space, with multiple successive acquisitions of individual k-values taken on a time scale on the order of microseconds, within a defined tissue volume, and subsequent combination of the multiple measurements in such a way as to maximize SNR. The reduced acquisition volume, and acquisition of only select values in k-space along selected directions, enables much higher in-vivo resolution than is obtainable with current MRI techniques.

REFERENCE TO RELATED APPLICATIONS

This application relies on the priority of U.S. provisional applicationsSer. No. 62/044,321 filed on Sep. 1, 2014 entitled SELECTIVE SAMPLINGMAGNETIC RESONANCE-BASED METHOD FOR ASSESSING STRUCTURAL SPATIALFREQUENCIES, Ser. No. 62/064,206 filed on Oct. 15, 2014 having the sametitle and Ser. No. 62/107,465 filed on Jan. 25, 2015 entitledmicro-Texture Characterization by MRI.

BACKGROUND

1. Field of the Invention

The herein claimed method relates to the field of diagnostic assessmentof fine textures in biological systems for pathology assessment anddisease diagnosis, and in material and structural evaluation in industryand in engineering research. More specifically, the invention employs amethod for repeat measurement of k-values associated with the spatialorganization of biologic tissue texture, with the MRI machine gradientsturned off. This allows assessment of tissue texture on a time-scale onthe order of a msec, whereby the problem of patient motion becomesnegligible. The method enables in vivo assessment, towards diagnosis andmonitoring, of disease and therapy-induced textural changes in tissue.Representative targets of the technique are: 1) for assessment ofchanges to trabecular architecture caused by bone disease, allowingassessment of bone health and fracture risk, 2) evaluation of fibroticdevelopment in soft tissue diseases such as, for example, liver, lung,and heart disease, and 3) changes to fine structures in neurologicdiseases, such as the various forms of dementia, or in cases of braininjury and downstream neuro-pathology as in, for example, TraumaticBrain Injury (TBI) and Chronic Traumatic Encephalopathy (CTE), or forcharacterization and monitoring of abnormal neurologic conditions suchas autism and schizophrenia. Other pathology applications includeassessment of vascular changes such as in in the vessel networksurrounding tumors or associated with development of CVD(Cerebrovascular Disease), and of changes in mammary ducting in responseto tumor growth. The invention also has applications in assessment offine structures for a range of industrial purposes such as measurementof material properties in manufacturing or in geology to characterizevarious types of rock, as well as other uses for which measurement offine structures/textures is needed.

2. Description of the Related Art

Though fine textural changes in tissue have long been recognized as theearliest markers in a wide range of diseases, robust clinical assessmentof fine texture remains elusive, the main difficulty arising fromblurring caused by subject motion over the time required for dataacquisition.

Early and accurate diagnosis is key to successful disease management.Though clinical imaging provides much information on pathology, many ofthe tissue changes that occur as a result of disease onset andprogression, or as a result of therapy, are on an extremely fine scale,often down to tens of microns. Changes in fine tissue texture have beenrecognized for many years by diagnosticians, including radiologists andpathologists as the earliest harbinger of a large range of diseases, butin vivo assessment and measurement of fine texture has remained outsidethe capabilities of current imaging technologies. For instance,differential diagnosis of obstructive lung disease relies on a texturalpresentation in the lung parenchyma, but the robustness of the ComputedTomography (CT) measure of early stage disease is limited. Trabecularbone microarchitecture, the determinant of fracture risk in aging bone,has also remained elusive due to image blurring from patient motionduring Magnetic Resonance (MR) imaging scans. Post processing analysisof MR-images is sometimes used to try to differentiate surface texturesin structures such as tumors and white matter. (DRABYCZ, S., et al.;“Image texture characterization using the discrete orthogonalS-transform”; Journal of Digital Imaging, Vol. 22, No 6, 2009. KHIDER,M., et al.; “Classification of trabecular bone texture from MRI and CTscan images by multi-resolution analysis”; 29th Annual InternationalConference of the IEEE Engineering in Medicine and Biology Society, EMBS2007.) But post processing analysis is limited in effect as it doesn'tdeal with the underlying problem that prevents high resolutionacquisition of textural information, i.e. subject motion. (MACLAREN, J.et al.; “Measurement and correction of microscopic head motion duringmagnetic resonance imaging of the brain”, PLOS/ONE, Nov. 7, 2012.MACLARAN, J. et al.; “Prospective motion correction in brain imaging: areview; Magnetic Resonance in Medicine, Vol. 69, 2013.)

The main sources of motion affecting MR imaging are cardiac pulsatilemotion, respiratory-induced motion and twitching. The first two arequasi-cyclic, the usual approach to which is gating at the slowest phaseof motion. However, even with gating, there is sufficient variationbetween acquisitions to cause loss of spatial phase coherence at thehigh k-values of interest for texture measurements. This problem isexacerbated by the fact that motion may not be perfectly cyclic, andoften originates from combined sources. Twitching is rapid, inducingrandom displacements, and hence it is not possible to maintain coherenceat the high k-values of interest when measuring texture.

While Positron Emission Tomography (PET) provides valuable diagnosticinformation, it is not capable of resolution below about 5mm and relieson the use of radioactive tracers for imaging as well as x-ray beams forpositioning, raising dose concerns, especially if repeat scanning isneeded. (BERRINGTON DE GONZALEZ, A. et al.; “Projected cancer risks fromComputed Tomographic scans performed in the United States in 2007”; JAMAInternal Medicine, Vol. 169, No. 22, December 2009.) Further, PETimaging is extremely costly, requiring a nearby cyclotron. CT resolutiondown to 0 7 mm is possible in theory, though this is obtained at highradiation dose and is subject to reduction by patient motion over thefew minute scan time. The non-negligible risk from the associatedradiation dose makes CT problematic for longitudinal imaging and limitsavailable resolution. Along with serious dose concerns, digital x-rayresolution is limited because the 2-dimensional image obtained is acomposite of the absorption through the entire thickness of tissuepresented to the beam. Current clinical diagnostics for the diseasesthat are the target of the method claimed herein are fraught withdifficulties in obtaining sufficient in vivo resolution, or accuracy. Insome cases, no definitive diagnostic exists currently. In otherpathologies, particularly in breast and liver, diagnosis is dependent onbiopsy, with its non-negligible risk of morbidity and even mortality,and which is prone to high read and sampling errors. (WELLER, C; “Cancerdetection with MRI as effective as PET-CT scan but with zero radiationrisks”; Medical Daily, Feb. 18, 2014.)

Bone health is compromised by aging, by bone cancer, as a side effect ofcancer treatments, diabetes, rheumatoid arthritis, and as a result ofinadequate nutrition, among other causes. Bone disease affects over tenmillion people annually in the US alone, adversely affecting theirquality of life and reducing life expectancy. For assessment of bonehealth, the current diagnostic standard is Bone Mineral Density (BMD),as measured by the Dual Energy X-ray Absorptiometry (DEXA) projectiontechnique. This modality yields an areal bone density integrating theattenuation from both cortical and trabecular bone, similar to theimaging mechanism of standard x-ray, but provides only limitedinformation on trabecular architecture within the bone, which is themarker linked most closely to bone strength. (KANIS, J. AND GLUER, C.;“An update on the diagnosis and assessment of osteoporosis withdensitometry”; Osteoporosis International, Vol. 11, issue 3, 2000.LEGRAND, E. et al.; “Trabecular bone microarchitecture, bone mineraldensity, and vertebral fractures in male osteoporosis”; JBMR, Vol. 15,issue 1, 2000.) BMD correlates only loosely with fracture risk. Apost-processing technique, TBS (Trabecular Bone Score) attempts tocorrelate the pixel gray-level variations in the DEXA image, to yieldinformation on bone microarchitecture. A comparison study determinedthat BMD at hip remains a better predictor of fracture. But, though TBSdoes not yield a detailed assessment of trabecular architecture.(BOUSSON, V., et al.; “Trabecular Bone Score (TBS): available knowledge,clinical relevance, and future prospects”; Osteoporosis International,Vol. 23, 2012. DEL RIO, et al.; “Is bone microarchitecture status of thespine assessed by TBS related to femoral neck fracture? A Spanishcase-control study”: Osteoporosis International, Vol. 24, 2013.) TBS isa relatively new technique and is still being evaluated.

Measurement of bone microarchitecture, specifically trabecular spacingand trabecular element thickness, requires resolution on the order oftenths of a millimeter. MRI, ultrasound imaging, CT, and microCT haveall been applied to this problem. In MRI, though high contrast betweenbone and marrow is readily obtained, resolution is limited by patientmotion over the long time needed to acquire an image with sufficientresolution to characterize the trabecular network. The finer the texturesize of this network, the greater the blurring from motion. An attemptto mitigate the effects of patient motion by looking only at theskeletal extremities, removed from the source of cardiac and respiratorymotion sources, has been tried using both MRI and microCT. However, thecorrelation between bone microarchitecture in the extremities and thatin central sites in not known. Further, a large data matrix, hence longacquisition time, is still required to obtain sufficient imageinformation to determine trabecular spacing and element thickness. Thislong acquisition time results in varying levels of motion-inducedblurring, depending on patient compliance—twitching is still a seriousproblem even when measuring extremities. A proposed MR-based technique,fineSA (JAMES, T., CHASE, D.; “Magnetic field gradient structurecharacteristic assessment using one dimensional (1D) spatial-frequencydistribution analysis”; U.S. Pat. No. 7,932,720 B2; Apr. 26, 2011.),attempts to circumvent the problem of patient motion by acquiring a muchsmaller data matrix of successive, finely-sampled, one-dimensional,frequency-encoded acquisitions which are subsequently combined to reducenoise. Imaging in this case is reduced to one dimension, reducing thesize of the data matrix acquired and, hence, the acquisition time.However, as the gradient encoded echoes, are very low Signal to Noise(SNR), noise averaging is required. Though some resolution advantage isgained by this method relative to 2 and 3-d imaging, the need to acquiremany repeat spatially-encoded echoes over several response times (TRs)for signal averaging results in an acquisition time on the order ofminutes—too long to provide motion immunity. Thus, resolutionimprovement obtainable by the technique is limited.

What is needed is an accurate, robust, non-invasive, in vivo measure oftrabecular spacing and trabecular element thickness capable of assessingbones in the central skeleton, as these are the key markers forassessing bone health and predicting fracture risk. Until now, noclinical technique has been able to provide this capability.

Fibrotic diseases occur in response to a wide range of biologicalinsults and injury in internal organs, the development of collagenfibers being the body's healing response. The more advanced a fibroticdisease, the higher the density of fibers in the diseased organ.Fibrotic pathology occurs in a large number of diseases, from lung andliver fibrosis, to cardiac and cystic fibrosis, pancreatic fibrosis,muscular dystrophy, bladder and heart diseases, and myelofibrosis, inwhich fibrotic structures replace bone marrow. Fibrotic development isattendant in several cancers, such as breast cancer. A differentpathology development is seen in prostate cancer, where the diseasedestroys healthy organized fibrous tissue. In all cases, texturalspacings highlighted in the tissue change in response to diseaseprogression, as collagen fibers form along underlying tissue structures.In liver disease, the textural wavelength changes as the healthy tissuetexture in the liver is replaced by a longer wavelength textureoriginating from the collagen “decoration” of the lobular structure inthe organ. In other organs/diseases, textural change reflects the upsetin healthy tissue with development of texture indicative of fibroticintervention.

To span the range of disease progression in most fibrotic pathologies,evaluation of textural changes from fibrotic development requiresresolution on the scale of tenths of a mm. One of the most prevalent ofsuch pathologies, liver disease, is representative of the difficulty ofassessing fibrotic structure. Currently, the gold standard for pathologyassessment is tissue biopsy—a highly invasive and often painfulprocedure with a non-negligible morbidity—and mortality—risk (patientsneed to stay at the hospital for post-biopsy observation for hours toovernight), and one that is prone to sampling errors and large readingvariation. (REGEV, A.; “Sampling error and intraobserver variation inliver biopsy in patients with chronic HCV infection”; American Journalof Gastroenterology; 97, 2002. BEDOSSA, P. et al.; “Sampling variabilityof liver fibrosis in chronic hepatitis C”; Hepatology, Vol. 38, issue 6,2004. VAN THIEL, D. et al.; “Liver biopsy: Its safety and complicationsas seen at a liver transplant center”; Transplantation, May 1993.)Ultrasound, another modality often used to assess tissue damage in liverdisease, is only able to provide adequate assessment in the later stagesof the disease—it is used to diagnose cirrhosis. MagneticResonance-based Elastography (MRE), which has been under development forsome time for use in assessment of liver disease, is not capable ofearly-stage assessment—the read errors are too large prior tosignificant fibrotic invasion (advanced disease). Further, thistechnique requires expensive additional hardware, the presence of askilled technician, and takes as much as 20 minutes total set up andscanning time, making it a very costly procedure. The ability to imagefibrotic texture directly by MR imaging is compromised both by patientmotion over the time necessary to acquire data and by lack of contrastbetween the fibers and the surrounding tissue. Even acquisition during asingle breath hold is severely compromised by cardiac pulsatile motionand noncompliance to breath hold, which results in significant motion atmany organs, such as liver and lungs. And SNR is low enough that motioncorrection by combining reregistered MR-intensity profiles obtained fromsuccessive echoes is extremely problematic. Similarly, assessment of theamount of cardiac fibrosis in early stage disease using MRI is seriouslyhampered by cardiac pulsation over the time of the measurement. Asmotion is, unlike Gaussian noise, a non-linear effect, it can't beaveraged out—there must be sufficient signal level to allowreregistration before averaging for electronic noise-reduction. A moresensitive (higher SNR), non-invasive technique, capable of assessingtextural changes throughout the range of fibrotic development, fromonset to advanced pathology, is needed to enable diagnosis andmonitoring of therapy response.

Onset and progression of a large number of neurologic diseases areassociated with changes in repetitive fine neuronal and vascularstructures/textures. However, ability to assess such changes in thebrain is only available post mortem. Currently, definitive diagnosis ofAlzheimer's Disease (AD) is by post mortem histology of brain tissue. ADand other forms of dementia such as Dementia with Lewy Bodies, motordiseases such as Amyotrophic Lateral Sclerosis (ALS), Parkinson'sdisease, conditions precipitated by Traumatic Brain Injury (TBI) such asChronic Traumatic Encephalopathy (CTE), as well as those caused by otherpathologies or trauma, or conditions that involve damage to brainstructures such as Multiple Sclerosis (MS), Cerebrovascular Disease(CVD), and other neurologic diseases, are often only diagnosable inadvanced stages by behavioral and memory changes, precluding the abilityfor early stage intervention. Further, conditions such as epilepsy andautism have been associated with abnormal variations in fine neuronalstructures, which, if clinically diagnosable, would allow targetedselection for testing therapy response.

Various in vivo diagnostic techniques are available for AD and otherdementias, but none of them are definitive. These techniques range fromwritten diagnostic tests, which are prone to large assessment errors, toPET imaging to assess amyloid plaque density or glucose metabolism (FDGPET). As discussed previously, PET imaging is extremely expensive,cannot provide high resolution, and relies on use of radioisotopes andpositioning x-ray beams, complicating approval for longitudinal use dueto dose concerns. Further, neither amyloid imaging nor FDG PET has beenshown to provide a definitive indication of AD. (MOGHBEL, M. et al.“Amyloid Beta imaging with PET in Alzheimer's disease: is it feasiblewith current radiotracers and technologies?”; Eur. J. Nucl. Med. Mol.Imaging.)

Use of CSF biomarkers for dementia diagnosis is painful and highlyinvasive and cannot differentiate signal levels by anatomic position inthe brain, as is possible with imaging biomarkers. As various forms ofdementia are found to have different spatial/temporal progressionthrough the brain, this is a serious drawback to use of liquid biopsy.Another disease associated with various forms of dementia is CVD(Cerebrovascular Disease), which induces cognitive impairment as aresult of reduced blood flow through blocked vessels leading to braintissue. Something capable of high-resolution assessment ofpathology-induced changes in micro-vessels is needed here.

Tissue shrinkage due to atrophy in many forms of dementia including ADis measurable with careful registration of longitudinally-acquired datain MRI, but the disease is advanced by the time this shrinkage ismeasurable. Early stages of disease are indicated in post mortemhistology by degradation in the columnar ordering of cortical neurons,the normal spacing for these columns being on the order of 100 micronsin most cortical regions. (CHANCE, S. et al.; “Microanatomicalcorrelates of cognitive ability and decline: normal aging, MCI, andAlzheimer's disease”; Cerebral Cortex, August 2011. E. DI ROSA et al.;“Axon bundle spacing in the anterior cingulate cortex of the humanbrain”; Journal of Clinical Neuroscience, 15, 2008.) This textural size,and the fact that the cortex is extremely thin, makes speed ofacquisition paramount, as even tiny patient motion will make datacollection impossible. Assessment of textural changes on the order oftens of microns microns is extremely problematic in vivo, but would, ifpossible, enable targeting a range of fine textural changes in neuronaldisease diagnosis and monitoring, and would play an important role intherapy development.

Another possible neurologic application for the claimed method is to, invivo, determine the boundaries of the various control regions of thecerebral cortex or the different Brodmann's areas of which these arecomprised. Such ability would greatly aid data interpretation in brainfunction studies, such as those performed using, for example, FMRI(Functional Magnetic Resonance Imaging).

The three classes of diseases listed above, bone disease, fibroticdiseases, and neurologic diseases are not an all-inclusive list. Otherdisease states in which pathology-induced changes of fine structuresoccur, for instance angiogenic growth of vasculature surrounding atumor, or fibrotic development and changes in vasculature and mammarygland ducting in response to breast tumor development, also arepathologies wherein the ability to resolve fine tissue textures wouldenable early detection of disease, and monitoring of response totherapy.

The ability to measure changes in fine textures would be of great valuefor disease diagnosis. Non-invasive techniques that do not rely on useof ionizing radiation or radioactive tracers allow the most leeway forearly diagnosis and repeat measurement to monitor disease progressionand response to therapy. Magnetic Resonance Imaging (MRI), whichprovides tunable tissue contrast, is just such a non-invasive technique,with no radiation dose concerns. However, in order to circumvent theproblem of signal degradation due to patient motion, data must be takenon a time scale not previously possible.

SUMMARY OF THE INVENTION

A method for selective sampling to assess texture using magneticresonance (MR) is accomplished by exciting a volume of interest. Allgradients are then turned off and multiple samples of an RF signalencoded at a specific k-value are recorded in a single TR. In certainembodiments, exciting the volume of interest is accomplished in anexemplary embodiment by transmitting a first RF pulse with a firstgradient activated for slice selection, transmitting a second RF pulsewith a second gradient activated, said second gradient chosen for sliceselective refocusing in a region defined by an intersection of the firstslice and a second slice, apply a selected gradient pulse shaped toinduce phase wrap to create a spatial encode with resulting pitchcorresponding to texture wavelength of the specific k-value and thentransmitting a third RF pulse with a third gradient activated. The thirdgradient is adapted to refocus excitation defining a region defined bythe intersection of the first and second slices and this third sliceselection. In yet a further embodiment, a selection pulse is issued on avector combination gradient to alter to a second k-value. The gradientis then again turned off and multiple samples of the RF signal arerecorded at the second k-value, while in the same TR. This embodimentmay be extended by issuing additional selection gradient pulses for apredetermined plurality of pulses on the vector combination gradientwith each pulse selecting a new k-value. With the vector combinationgradient turned off after each pulse, multiple samples of the RF signalat the next k-value induced at each negative pulse are recorded whilestill in the same TR.

An additional embodiment provides for transmitting a refocusing RF pulsewith the third gradient activated and then issuing a plurality ofselection gradient pulses on the vector combination gradient, each pulseselecting a new k-value. After each pulse of the vector combinationgradient, multiple samples of the RF signal at the next k-value inducedare recorded, again still in the same TR. This embodiment may be furtherrefined by transmitting a second refocusing RF pulse with the thirdgradient activated and then issuing a plurality of selecting gradientpulses on the vector combination gradient, with each pulse selecting anew k-value. With the vector combination gradient turned off after eachpulse, multiple samples of the RF signal at the next k-value induced ateach pulse are recorded.

An additional embodiment provides for recalling the signal multipletimes using a GRE (Gradient Recalled Echo), thereby extending the recordwindow to allow acquisition of additional k-value samples.

In an extension of the described embodiments, the number of samples ateach new k-value following each of the plurality of pulses on the vectorcombination gradient is selected based upon expected SNR, pathology,tissue contrast, texture size or texture bandwidth.

Additionally for the embodiments, the volume of interest in the step ofexciting a volume of interest is selected using the set of intersectingslice-selective refocusing, selective excitation using phased-arraytransmit in combination with appropriate gradients, adiabatic pulseexcitation to scramble signal from the tissue outside a region ofinterest, outer volume suppression sequences, and other methods ofselectively exciting spins in an internal volume including physicallyisolating the tissue of interest.

The initial embodiment may be employed wherein the steps of exciting avolume of interest, turning off all gradients and recording samples ofan RF signal at a specific k-value are repeated multiple times withinthe same TR or in succeeding TRs to build a magnitude power spectrum,the specific k-values in each TR independent of intervening motion ifconstrained within the volume of interest for each TR.

Alternatively, the steps of exciting a volume of interest, turning offall gradients and recording samples of an RF Signal at a specifick-value are repeated multiple times within the same TR or in succeedingTRs with variation of the volume of interest thereby acquiring texturaldata to assess a textural variation within or across an organ oranatomy.

BRIEF DESCRIPTION OF THE DRAWINGS

The features and advantages of embodiments disclosed herein will bebetter understood by reference to the following detailed descriptionwhen considered in connection with the accompanying drawings wherein:

FIG. 1 is a simulation showing the number of data samples required foraveraging to achieve an output SNR≧20 dB as a function of input SNR;

FIG. 2 is a simulation showing the number of data samples needed foraveraging to achieve a SNR≧20 db as a function of location in k-space;

FIG. 3 is an example timing diagram of a pulse sequence for the claimedmethod showing the timing of a single TR;

FIG. 4 is a close-up of the example timing diagram of FIG. 3;

FIG. 5 is an example of a timing diagram for the claimed method,designed to acquire multiple measures of a select set of k-values, witha different number of samples acquired at each k-value to counteract thedecrease in energy density at increasing k-value;

FIG. 6 is a simulation showing that the ability provided by the claimedmethod to acquire many repeats of a targeted k-value within a single TRenables robust signal averaging to boost SNR;

FIG. 7 is a simulation showing the results of attempting to acquire 90samples for averaging using the conventional frequency-encoded echoapproach, wherein acquisition of only a small number of repeats of aparticular k-value are possible in each TR due to the long record timefor each echo;

FIG. 8 is an example timing diagram for the claimed method designed toprovide data acquisition over multiple refocused echoes within a singleTR; and,

FIGS. 9A and 9B are a depiction of two possible shapes for theacquisition volume of interest (VOI).

DETAILED DESCRIPTION OF THE INVENTION

The following definition of terms as used herein is provided:

-   180° inversion pulse RF pulse that inverts the spins in a tissue    region to allow refocusing of the MR signal.-   180° pulse An RF pulse that tips the net magnetic field vector    antiparallel to B₀-   90° pulse An RF pulse that tips the net magnetic field vector into    the transverse plane relative to B₀-   3T 3 Tesla-   A/D Analog to digital converter-   AD Alzheimer's Disease-   ADC Average diffusion coefficient measured in Diffusion Weighted    Imaging-   Adiabatic pulse excitation Adiabatic pulses are a class of    amplitude- and frequency-modulated RF-pulses that are relatively    insensitive to B1 inhomogeneity and frequency offset effects.-   AWGN Additive White Gaussian Noise Additive white Gaussian noise    (AWGN) is a basic noise model used in Information theory to mimic    the effect of many random processes that occur in nature.-   Biopsy A biopsy is a sample of tissue extracted from the body in    order to examine it more closely.-   C/N Contrast to Noise, a measure of image quality based on signal    differences between structural elements rather than on overall    signal level-   CAWGN Complex-valued, additive white Gaussian noise-   CBF Cerebral Blood Flow-   Chemical shift Small variations in MR resonant frequency due to the    different molecular environments of the nuclei contributing to an MR    signal.-   CJD Creutzfeld-Jakob Disease-   Crusher gradients Gradients applied on either side of a 180° RF    refocusing slice selection pulse to reduce spurious signals    generated by imperfections in the pulse.-   CSF Cerebrospinal fluid-   DEXA Dual Energy X-ray Absorptiometry is a means of measuring bone    mineral density using two different energy x-ray beams.-   DSC Dynamic susceptibility contrast-   DTI Diffusion Tensor Imaging-   DWI Diffusion Weighted Imaging-   Echo The RF pulse sequence where a 90° excitation pulse is followed    by a 180° refocusing pulse to eliminate field inhomogeneity and    chemical shift effects at the echo.-   Frequency encodes Frequency-encoding of spatial position in MRI is    accomplished through the use of supplemental magnetic fields induced    by the machine gradient coils-   Gaussian noise Gaussian noise is statistical noise having a    probability density function (PDF) equal to that of the normal    distribution, which is also known as the Gaussian distribution.-   Gradient pulse a pulsing of the machine magnetic field gradients to    alter the k-value encode-   Gradient set the set of coils around the bore of an MR scanner used    primarily to spatially encode signal or to set a particular phase    wrap in a selected direction-   GRE Gradient Recalled Echo-   Interleaved acquisition Signal acquisition from a multiplicity of    VOIs, successively excited within a single TR-   Isochromat A microscopic group of spins that resonate at the same    frequency.-   k-space The 2D or 3D Fourier transform of the MR image.-   k-value coefficient The coefficient in a Fourier series or transform    reflecting the relative weight of each specific k-value in the    series.-   k-space The 2D or 3D Fourier transform of the MR image.-   k-value One of the points in k-space reflecting the spacing of    structural elements in a texture field.-   k-value selection pulse The gradient pulse used to select a specific    k-value encode along the sampled direction-   Library of k-space values the net collection of k-space coefficients    acquired in a particular region of tissue for tissue    characterization-   Machine gradients the magnetic field gradients imposable through use    of the set of gradient coils in an MR scanner-   MRE Magnetic Resonance Elastography—an imaging technique that    measures the stiffness of soft tissues using acoustic shear waves    and imaging their propagation using MRI.-   MRI Magnetic Resonance Imaging-   MS Multiple Sclerosis-   Noise floor In signal theory, the noise floor is the measure of the    signal created from the sum of all the noise sources and unwanted    signals within a measurement system-   PET Positron Emission Tomography is a functional imaging technique    that produces a three-dimensional image of functional processes in    the body using a positron-emitting radiotracer.-   Phase coherence (spatial) When referring to multiple measurements    within a common VOI of a or multiple k-values indicates that the    sample has the same position relative to the measurement frame of    reference-   Phase encode A phase encode is used to impart a specific phase angle    to a transverse magnetization vector. The specific phase angle    depends on the location of the transverse magnetization vector    within the phase encoding gradient, the magnitude of the gradient,    and the duration of the gradient application.-   Phase wrap The helical precession of the phase of the transverse    magnetization along a phase encoded sample-   Pitch with reference to the pitch of a screw, the tightness of the    phase wrap along the direction of k-value encode-   Profile A one dimensional plot of signal intensity-   RF Radio Frequency electromagnetic signal-   Semi-crystalline texture a texture exhibiting regular spacing along    one or more directions-   slice (slab) Used interchangeably to indicate a non-zero thickness    planar section of the-   Slice-selective refocusing Refocusing of spins through combination    of a slice selective gradient and an RF pulse such that the    bandwidth of the RF pulse selects a thickness along the direction of    the gradient, and the RF pulse tips the net magnetization vector    away from its equilibrium position Only those spins processing at    the same frequency as the RF pulse will be affected.-   SE Spin Echo-   SNR Signal to Noise Ratio-   Spoiler gradients see crusher gradients-   T2 Defined as a time constant for the decay of transverse    magnetization arising from natural interactions at the atomic or    molecular levels.-   T2* In any real NMR experiment, the transverse magnetization decays    much faster than would be predicted by natural atomic and molecular    mechanisms; this rate is denoted T2*(“T2-star”). T2* can be    considered an “observed” or “effective” T2, whereas the first T2 can    be considered the “natural” or “true” T2 of the tissue being imaged.    T2* is always less than or equal to T2.-   TBS Trabecular Bone Score is a technique that looks for texture    patterns in the DEXA signal for correlation with bone    microarchitecture for assessing bone health-   TE Spin Echo sequences have two parameters: Echo Time (TE) is the    time between the 90° RF pulse and MR signal sampling, corresponding    to maximum of echo. The 180° RF pulse is applied at time TE/2.    Repetition Time is the time between 2 excitations pulses (time    between two 90° RF pulses).-   Textural frequency the number of texture wavelength repeats per unit    length in a texture-   Texture wavelength the characteristic spacing between structural    elements in a texture-   TR Spin Echo sequences have two parameters: Echo Time (TE) is the    time between the 90° RF pulse and MR signal sampling, corresponding    to maximum of echo. The 180° RF pulse is applied at time TE/2.    Repetition Time is the time between 2 excitations pulses (time    between two 90° RF pulses).-   Vector combination gradient A magnetic gradient resulting from any    vector combination of the gradient coil set-   VOI Volume of Interest-   Windowing function In signal processing, a window function (also    known as an apodization function or tapering function) is a    mathematical function that is zero-valued outside of some chosen    interval-   x-ray diffraction X-ray diffraction is a tool used for identifying    the atomic and molecular structure of a crystal

The embodiments disclosed herein provide an MR-based technique thatenables in vivo, non-invasive, high-resolution measurement andassessment of fine biologic textures, enabling monitoring of textureformation and/or change in response to disease onset and progression ina range of pathologies. This same method can be applied to fine-texturecharacterization in other biologic and physical systems. It enablesMR-based resolution of fine textures to a size scale previouslyunattainable in in vivo imaging. The method, while described herein withrespect to biological systems for examination of tissue, is equallyapplicable for assessment of fine structures in a range of industrialpurposes such as measurement of material properties in manufacturing orin geology to characterize various types of rock, as well as other usesfor which measurement of fine structures/textures is needed.

The method claimed herein achieves this significant improvement in invivo resolution of fine texture by acquiring the requisite data fastenough that the effect of subject motion, the factor that limits MRIresolution, becomes negligible. This fast acquisition is achieved byacquiring data incrementally--at a single location, orientation and atone, or a select set, of k-values at a time—within one TR. Afterapplying an encoding gradient to select the k-value of interest, data isacquired with the gradient switched off, allowing multiple acquisitionrepeats of the encoded k-value for subsequent averaging to reduceelectronic noise, thus enabling robust measure of individual k-valuesbefore motion blurring can occur. To build up measurements on a largerset of selected k-values present within the tissue, or towardsdevelopment of a continuous spectrum of textural spacings within thetissue, the acquisition TR can be repeated as many times as necessary,changing the encode as needed to span the desired extent of real and ofk-space required. The set of one or more k-values output from each TRare now high SNR due to the ability to average repeats without motioneffects, and since the measure of interest is textural spacing, and notdevelopment of an image, the lack of phase coherence between TRs is ofno concern.

In its simplest form the method claimed herein consists of acquiring MRsignal from within an inner volume to encompass a specific tissue regionof interest, such as a lesion, an organ, a location in an organ, aspecific region of bone, or a number of regions in a diseased organ forsampling. This inner volume may be excited by one of a number ofmethods, including but not limited to: intersecting slice-selectiverefocusing, selective excitation using phased-array transmit incombination with appropriate gradients, adiabatic pulse excitation toscramble signal from the tissue outside the region of interest, outervolume suppression sequences, and other methods of selectively excitingspins in an internal volume including physically isolating the tissue ofinterest, to name a few,

After definition of a volume of interest (VOI), the gradient is turnedoff, and multiple samples of signal centered at a specific k-value, thespread of which is defined by receiver BW and sampling length, areacquired. This measurement is repeated only in specified directionswithin the VOI rather than trying to map all of k-space, as is requiredto generate an image. One or more samples of a particular k-value areacquired within an acquisition block during a single TR and the k-valuesubsequently incremented or decremented, allowing further multiplesamples of other k-values as desired during the same TR. This methodallows multiple sampling of each k-value of interest over a time periodof milliseconds, providing immunity to subject motion. The process canthen be repeated in further TRs, the requirement on motion betweenk-value acquisitions being only that the VOI remain within the tissueregion of interest. Build up of a magnitude spectrum of spatialfrequencies may be accomplished without the need to acquire it in aspatially coherent manner. Because the quantities of interest are therelative intensities of the various k-values (textural spacings) presentin the sample volume, as long as the acquisition volume remains within arepresentative sample of tissue, any motion between the blocks does notcompromise the measurement. In the case where motion of sufficientlylarge magnitude that the internally excited volume could move into othertissue volumes over the course of building up a spectrum of k-valuescontained in the tissue, use of fairly robust, real-time piloting andacquisition algorithms can be used for gross repositioning of theinternal selectively excited volume and for rejecting data sets thathave failed to stay in the proper tissue.

Repositioning the VOI to allow sampling of texture at multiple positionswithin or across an organ or anatomy allows determination of thevariation in pathology through the organ. The data acquired can, withreference to positioning images, be mapped spatially. Either the VOI canbe moved in successive TRs or interleaved acquisition done within asingle TR by exciting additional volumes during the time that the signalis recovering in advance of the next TR. The requirement is thatsuccessive VOIs be excited in new tissue, that does not overlap theprevious slice selects. Spatial variation of pathology can be determinedby this method. This can also be used to monitor temporal progression ofa pathology through an organ if the measure is repeated longitudinally.

Tailoring the pulse sequence to pre-wind phase in the sample volumepositions the highest k-values of interest at the echo peak where thesignal is strongest, providing best SNR measurement.

Sampling of k-values along multiple directions at varying angles andalong varying paths, either rectilinear or curved, within the volumeunder study can yield important information on texture, especiallytextures with semi-ordered structure in specific directions, such asneuronal minicolumns. Measurement of the k-values associated withcolumnar spacing is extremely sensitive to alignment of the samplingpath, as slight variations in sampling direction on either side ofperpendicular show a rapid drop off in signal magnitude for thatk-value. Rocking the acquisition path on either side of the signalmaximum can yield a measure of pathology-induced randomness which isindicated by the width of the peak.

With the gradient switched off for data acquisition, tuning thebandwidth to particular chemical species can enhance structuralinformation when the chemical composition of the structure under studyis known.

The method claimed herein can be used in conjunction with time-dependentcontrast schemes that target blood flow. Some of these contrasttechniques are Blood Oxygenation Level Dependent (BOLD) imaging,Arterial Spin Labeling (ASL) imaging, and Dynamic SusceptibilityContrast (DSC) imaging. As these methods use various techniques tohighlight vasculature, changes in the texture of the vasculatureassociated with many pathologies, including CVD (cerebrovasculardisease) and tumor growth can be measured.

The method claimed herein can also be used in conjunction with otherMR-based measurement techniques, including DWI and DTI, to provide frontend information toward parameter selection for the diffusion techniquesas well as correlation with their measurements of tissue health.

The rapid repeat measurement of a single k-value, with the total time toacquire a block being on the order of a msec, reduces patient andmachine motion-induced blurring to a negligible level, enabling robustassessment of fine textures previously not accessible in vivo. (Forcomparison, standard MR image acquisition times are much longer induration over which patients are asked to remain completely stationary.)The SNR of each k-value measured is significantly improved throughcombination of the individual samples at each k-value within a block;this averaging can now can be done without concern for subject motion,which is eliminated due to the rapid sequential acquisition of theindividual samples in the block.

This significant improvement in SNR is made possible because the methodclaimed herein focuses on acquiring only the k-values of interest fordetermination of fine texture pathology signatures rather than onacquisition of the large number of entire spatially-encoded echoesrequired for image formation. The significantly reduced data matrixenables the increased number of repeats at the targeted k-values, andhence significant improvement in SNR.

Energy density within a range of textural spacings is proportional totextural wavelength, or inversely to k-value—i.e. the higher thek-value, the lower the associated signal intensity. The fast acquisitionenabled through use of the method claimed herein, enables tailoring thenumber of acquisition repetitions at a particular k-value to acquirek-values for which there is low signal first, before T₂ and T₂* effectshave degraded signal amplitude. In this way, the SNR of each repeat tobe averaged for noise cancellation (or spatial-phase-corrected beforecombining it with the measurements of k-value from subsequent TRs) willbe above this threshold. It does not matter that there is motion betweenacquisition cycles at different k-values as long as each acquisitionlies within the tissue volume of interest (VOI). As the claimed methodtargets only assessment of pathology-induced changes in tissue texture,there is no requirement for phase coherence over an entire cycle of dataacquisition, as is required in imaging.

Several benefits result from acquiring data after the gradient isswitched off for single-k-value sampling in a reduced volume (the VOI).By proper pulse sequencing, the echo record window can be designed suchthat recording begins with the highest k-values of interest, as signallevel is highest at the echo peak. This enables recording of finestructures currently unachievable with in vivo MR imaging.

Additionally, T2* is longer with the gradient off, so SNR is improved bythe longer acquisition times possible This allows acquisition of anincreased number of samples, N.

Coil combination is also simplified by having higher SNR for eachk-value, hence providing a significant improvement in overall SNR. Thisis especially beneficial as the trend in MRI is towards coil arrayscomposed of many small element coils. As the acquisition volumestargeted in the method claimed herein are small, correction for phaseacross the sample volume is not needed. Only one phase and gain valuefor each coil is needed for combining the multiple element channels.These can be combined using the Maximal Ratio Combining (MRC) method,which weights the coil with the highest SNR most heavily, or othermulti-signal combination methods. (Phase and gain for the elements of agiven coil array can be determined once from a phantom and applied topatient data.)

Signal acquisition and data sampling in a standard MRI scan is done byacquiring complex-valued samples of multiple echoes, while applying agradient sequence concurrently, as well as in sequence with the echoes.Imaging relies on frequency encode for one of the dimensions becausethis allows a line in k-space to be acquired with each phase encoderather than a single point. For 3-dimensional imaging, two dimensions ink-space normally rely on phase encode to generate the targeted fillingof k-space, with the third dimension frequency-encoded. Phase encodeacquisition in imaging usually entails acquisition of on the order of256 k-values in each of the phase-encode directions, hence is is arelatively slow process. Clinical MRI scans take on the order of 10-15minutes to generate an image. The aim in image construction is toacquire sufficient k-space coverage to fill out all the coefficients inthe 2 or 3-dimensional Fourier series, which is why in standard MRresolution is limited by subject motion.

The method claimed herein is in direct contrast to standard MR dataacquisition, with its focus on image generation. Image formation isplagued by blurring resulting from subject motion over the long timenecessary to acquire the large data matrix required. Since the target ofthe method claimed herein is texture rather than image, the onlyrequirement on subject motion is that the sampled volume remain within aregion of similar tissue properties over the course of acquiring data.This is a much less stringent and easy to achieve target than therequirement of structural phase coherence, as the scale of the allowablemotion is then large enough, and of a temporal order, to be easilycorrectable by real-time motion assessment and correction techniques.The speed of acquisition for the method claimed herein is such that, inmost cases, real-time motion correction may not be necessary at all.While other methods have focused on post-processing of images to try toextract textural measures, the method claimed herein eliminates the needfor image generation, focusing instead on directly measuring texture,hence enabling a more sensitive and robust measure.

Frequently, k-space sampling is considered synonymous with sampling ofan echo in the presence of a gradient set. In the method claimed herein,the approach to k-space filling is to acquire only the set of k-valuesneeded for texture evaluation in the targeted pathology, with dataacquired after the gradient is switched off. This method enables suchrapid acquisition of single-k-value repeats for averaging for noisereduction that subject motion does not degrade the data.

Along with the huge improvement in SNR that arises from samplingk-values individually, with many repeats of a select set of k-valuesacquired in a single TR, acquisition after the gradient is switched offallows further significant improvement in SNR and hence, increase inmeasurement robustness. This is explained in the following discussion.

MR echo sampling provides specific samples vs. time of a time-dependentecho. The echo is comprised by the gradients applied concurrently (forthe frequency-encode axis) and prior to (for a phase encode axis), butalso contains the isochromats associated with the different chemicalspecies of the sample, as well as the envelope (T2 & T2*) associatedwith spin-spin interactions.

Conventional frequency-encoded spin acquisitions impose a time-varyinggradient upon the sample, which effectively travels in k-space along apre-defined path. For rectilinear sampling, the path is along a straightline.

Frequency encodes generate only one measurement at a given k-value—at agiven point in time, the acquired sample of the echo represents the onevalue which corresponds to the Fourier coefficient at a specific k-spacelocation. The next echo sample represents the value at a differentk-space location, the next k-value dependent on the slope of thegradient applied concurrently. As long as there is sufficient signal atthe corresponding k-value, this approach works well. However, in caseswhere the signal of interest is near or even below the noise floor,usually additional samples and subsequent post-processing will berequired.

One way to reduce the noise floor in a frequency-encoded gradientread-out is to reduce the gradient strength and lower the receiverbandwidth. Decreasing the receiver bandwidth will indeed decrease thenoise level, and improve lower signal level detection (proportional tothe term k_(B) TB, with k_(B)corresponding to Boltzmann's constant, Tcorresponding to Temperature in Kelvin, and B is the receiver bandwidthin Hz.) However, this comes at the expense of larger chemical shiftartifacts.

Chemical shift artifacts arise as a consequence of the differentisochromats associated with different chemical species within thebiological sample. In a frequency-encoded k-space read-out, thosechemical species which resonate at a slightly higher frequency willappear to be displaced from their actual location in image space towardsthe direction of increasing frequency. If the spatial frequency encodinggradient is shallow, the apparent displacement can be quite large.

As such, to minimize chemical shift artifacts, the gradient slope istypically made as steep as possible to minimize the apparent shift towithin a narrow range (i.e. within 1 or two pixels in the image domain).However, this then requires a larger receiver bandwidth to accommodatethe larger frequency range. This in turn increases the overall noisefloor at a level proportional to the receive bandwidth.

The conclusion is that frequency readouts generally force a trade-offbetween gradient strength, noise level, and chemical shift artifacts.

A common technique for noise reduction in signal acquisition is throughrepeat sampling of a signal and subsequent combination of the multiplemeasurements. For linear noise sources, such as Gaussian noise, thistechnique improves SNR through cancellation of the random noise on thesignal, the cancellation effect increasing with the number of samples,N.

Noise reduction by this cancellation technique works for staticsubjects. However, motion-induced blurring is a non-linear effect, sosignal combining for which the individual measurements have shiftedthrough large spatial phase angles (relative to the textural/structuralwavelengths under study) does not lead to an improved SNR. A fairlystandard technique to correct for motion is to look at the MR intensitydata in real space and reregister successive traces/images to each otherto maximize overlap. It is assumed that, as with the reduction in whitenoise, linear combination of these reregistered signals will result inreduction of the blurring caused by the motion. However, this only worksif the SNR on each individual acquisition is high enough. Reregisteringlow SNR samples results in a high variance in the estimated position.Threshold theory defines that combining reregistered signals withnon-linear blurring, when the original signals are below a certain noisethreshold, only increases signal error.

The nonlinearity introduced by subject motion increases at higherk-values, since the motion-induced textural phase shift increases withk—i.e. as the size of the structures of interest decrease, the adverseconsequence of motion become more acute. This implies that the multiplesamples to be combined need to be derived from the same acquisitionsequence, acquired in a sufficiently short time span, to ensure there isnegligible motion between samples.

The Cramér-Rao Lower Bound provides insight into the number of samplesthat are required for a lower bound on the residual variance of anestimate, i.e. the SNR vs. number of samples, in Additive White GaussianNoise (AWGN). For low source SNRs in AWGN, one needs a large number ofsamples to average in order to obtain a usable SNR. The primaryassumption is that multiple acquisitions can be taken, then averaged toachieve the higher SNR. (CRAMÉR, H.; “Mathematical Methods ofStatistics”; Princeton University Press, 1946. RAO, C. R., “Informationand the accuracy attainable in the estimation of statisticalparameters”; Bulletin of the Calcutta Mathematical Society 37, 1945.)

Referring to the drawings, the graph in FIG. 1 comparing output SNRshown in trace 102 with number of samples required shown in trace 104demonstrates that, for high input SNRs, a single sample is sufficient toyield a low noise measure. For lower SNRs, multiple samples are requiredto “average out” the noise contribution. The ability to combine thesamples explicitly assumes that the underlying signal of interest isrelatively constant during the multiple sample acquisition process (i.e.the only component which changes is the noise).

The graph in FIG. 2 is a simulation with signal model, trace 202,providing an input SNR, trace 204, showing number of samples of k-value,trace 206, needed to yield a SNR of 20 dB as a function of location ink-space, given an input noise level of 3 mV rms. Since spectral energydensity is generally proportional to k⁻¹, to maintain adequate SNR alarger number of input samples is required at higher spatial frequencies(higher k-values). The noise level for the simulation is adjusted for˜10 dB SNR at k=2 cycles/mm (λ=500 μm).

As pointed out above, this type of averaging is possible for purelystatic samples with no displacement or deformation of the targetedtissue occurring over the temporal span of data acquisition. However,for in vivo applications, natural motion occurs even if the patient iscompliant. As the texture spacing of interest decreases, the adverseconsequences of motion become more acute. More to the point, this typeof averaging is based upon the assumption that the underlying signal isthe same across acquisitions, and that only the zero-mean,complex-valued, additive, white Gaussian noise (CAWGN) changes. If thesignal itself changes, the result will be an average, not only of thenoise, but also of the N different versions of the underlying signal,which really doesn't improve SNR.

Using low SNR samples to estimate and correct for motion will result ina high variance of the estimated position. This in turn yields a largevariance in the “corrected” acquisitions and does not yield theanticipated increase in SNR when these acquisitions are averaged. Thisimplies that the multiple samples need to be derived from the sameacquisition sequence, where motion between samples is extremely small.This is enabled by the method claimed herein.

The issue becomes more acute with shorter structural wavelengths.Consider two acquisitions, noise-free for the moment, one of which hasbeen displaced by an amount d. For a given k-value, an attempt toaverage them produces:

Y(2πk):=S(2πk)[1+e ^(−j2πkd)]/2   (0.1)

Where S(2πk) the complex-valued signal, and Y(2πk) represents theaverage of the two acquisitions.

This can be expressed as:

Y(2πk):=S(2πk)e ^(−jπkd) cos(πkd)   (0.2)

Which shows both a magnitude attenuation and phase shift, due to thedisplacement d. Limiting the magnitude attenuation to a floor value a,where 0<a<1, limits d to:

$\begin{matrix}{{d} \leq \frac{\cos^{- 1}(a)}{\pi \; k}} & (0.3)\end{matrix}$

This shows that, for a given magnitude error, the allowable displacementdecreases with increasing values of k. This is because, the smaller thetextural spacing of interest, the less motion can be tolerated over thecourse of data acquisition.

To deal with this problem, an alternate approach is taken in the methodclaimed herein, which is to dispense with the frequency-encoded readoutand to sample specific k-space points, acquiring one or multiplemeasurements at each k-value of interest at a single spatial locationand orientation at a time.

Within a given acquisition in standard MR practice, there are M sampleswhich are acquired of the echo. Instead of acquiring a sample at eachk-value, N<M of those samples could be used for estimation of the(complex-valued) underlying signal value at a specific k-value. Multiplesamples within an acquisition can be combined with much less concern ofmovement than across acquisitions because they are much closer in time.

If the entire echo is used to measure one k-value, the receive bandwidthcan be adjusted so as to pass the most abundant resonant peaks in theunderlying NMR spectrum, and attenuate frequencies above them.

Taking a straight MRS spectrum (no structural phase encodes), wouldyield a spectrum consisting primarily of peaks corresponding to H₂O(with a chemical shift of δ=4.7 ppm), as well as Carbon-Hydrogen bondswhich occur in fat (e.g. CH3, CH2, CH═CH, etc.), each with a differentchemical shift ranging from 0.9-5.7 ppm, with the most abundantresonance coming from CH2 in the aliphatic chain which occurs at δ=1.3ppm.

Assuming use of a 3T machine, since the Gyromagnetic ratio of Hydrogenis γ=42.576MHz/T, the chemical shift values are in the range of 166 Hz(for CH2) to 600.3 Hz (for H2O). As long as a (single sided) receiverbandwidth in excess of 600.3 Hz is used, the H2O peak will pass.Assuming baseband sampling, this implies a sampling rate >1.2 kHz (note,if complex base-band sampling is used, this could theoretically bereduced by about ½) The point here is that a narrow bandwidth can beused by this method, and sample rates on the order of 800 μs. Noise onthe signal is thereby reduced and multiple repeats of the k-valueacquisition data are acquired in milliseconds, thereby making theacquired data immune to patient motion. For comparison, a single imagingacquisition is made with a TE of ˜30 ms, and TR on the order of 500ms-2000 ms. To acquire the repeats necessary for signal averaging cantake minutes—a temporal range wherein respiratory, cardiac, andtwitching motion limits resolution through motion-induced blurring. Theclaimed method enables acquisition of values in regions of k-space whichhave very low signal levels, such as would be found for higher k-values(shorter textural-wavelengths) —the fine texture range that has hithertoremained elusive.

To maximize the signal, the non-zero frequencies of abundance areselected. In general, this does not correspond to a mere averaging ofall of the samples acquired. Instead it is akin to a matched filterwhich is “tuned” to the frequency of interest, corresponding to thespecific chemical species of interest.

As a side note, the full NMR spectrum may be extracted (without anyphase encoding gradients: just volume selection) to obtain a baseline ofthe underlying signal strength (and associated frequencies), which inturn will be spatially modulated, providing insight into texturalwavelengths through knowledge of the chemical species expected in thetextural elements under study.

The isochromats of interest can be extracted by acquiring N samples ofthe echo, then taking the Fourier transform. Since the echo is beingplayed out with no gradient, the strength of the resulting signal at theIsochromat of interest will correspond to the (complex-valued) k-valuecoefficient of interest.

Given the goal is to extract the relative magnitude of texturalwavelengths, just the magnitude vs. textural wavelength measurement isthe required information. However, in order to extract sufficient signalstrength and differentiate it from the underlying noise floor, thecomplex phasor values must be preserved until the end.

The relationship between the noise floor, the signal strength (at aspecific isochromat where there is an abundance of chemical species),the number of samples required, and the max tolerated error can beapproximated as

$\begin{matrix}{N \geq \frac{\sigma^{2}}{{A}^{2}ɛ^{2}}} & (0.4)\end{matrix}$

Where σ² represents the noise variance, |A|² represents the squaredmagnitude of the isochromat(s) of interest, and 0<Ε<1 represents theallowable error of the estimate. Further assuming that the noise ismostly sourced from the biological sample, this can be furtherapproximated as:

$\begin{matrix}{N \geq \frac{{{NF}_{eff} \cdot k_{B}}{TB}}{{A}^{2}ɛ^{2}}} & (0.5)\end{matrix}$

Where NF_(eff) is the effective noise figure of the receiver, k_(B) isBoltzmann's constant, T is the temperature in Kelvin of the biologicalsample, and B is the receiver bandwidth. In this case, N can be used asa guide to the number of samples that need to be acquired within a givenacquisition in order to create a reasonable estimate.

If the number of samples required exceeds the number available in oneacquisition, combination of measurements from a single acquisition maybe needed to maximize the signal, prior to spatial reregistrationbetween acquisitions. A reasonable estimate and displacement correctionbetween the two or more acquisition sets is needed. Combination ofmeasurements at a single k-value from a single TR block can now be usedto boost the SNR such that reregistration between successive TRs has amuch greater chance of success.

While the entire set of samples acquired in an echo or entire TR couldbe allocated to the estimate of one coefficient in k-space, ifacceptable values can be estimated using fewer than the maximum numberof echo samples, it opens up the possibility of being able to acquiremore than one coefficient in k-space within a specific echo or TR.

FIG. 3 shows an example timing diagram for a pulse sequence for dataacquisition using the method claimed herein. RF pulses included in trace302 are employed to excite selected volumes of the tissue underinvestigation, as in typical MR imaging. A first RF pulse, 304, istransmitted coincidentally with a gradient pulse 308 on the a firstmagnetic field gradient, represented in trace 306 . This excites asingle slice, or slab, of tissue the positioning of which is dependenton the orientation and magnitude of the first gradient, and thefrequencies contained in the RF pulse. The negative gradient pulse,pulse 310, rephases the excitation within the defined thickness of theslice or slab.

A second RF pulse 312, at twice the magnitude of first RF pulse 304, istransmitted coincidentally with gradient pulse 316, on a secondgradient, represented in trace 314, exciting a slice-selective refocusof spins, this second tissue slice intersecting with the first. (As thissecond RF pulse 312 tips the net magnetic vector to antiparallel to B₀,it results in inversion of spins and subsequent refocusing, thus leadingto a signal echo at a time after the 180 degree RF pulse equivalent tothe time between the 90° and 180° RF pulses.) An initial higher valuegradient pulse, 318, at the start of gradient pulse 316 is a crusher, or“spoiler” gradient, designed to induce a large phase wrap across thetissue volume. A similar gradient pulse, 322, at the trailing end ofpulse 316, as it comes after the 180 degree RF inversion pulse, unwindsthis phase wrap. In this way, any excitation that is not present priorto the 180 degree RF pulse, such as excitations from imperfections inthe 180 pulse itself, will not have this pre-encode so will not berefocused by the second crusher, hence will not contribute to thesignal. In summary, the second RF pulse, in combination with the appliedsecond gradient, provides slice selective refocusing of the signal in aregion defined by the intersection of the first slice and the secondslice set by this second gradient.

An encoding gradient pulse 326, on trace 314, sets an initial phasewrap, hence k-value encode, along the direction of gradient pulse 326.In general, the k-value encode can be oriented in any direction, byvector combination of the machine gradients but for ease ofvisualization is represented as on the second gradient.

A refocusing third RF pulse 328, applied in combination with gradientpulse 332 on a third gradient, represented by trace 330, defines a thirdintersecting slice selective refocus to define the VOI. Gradient pulse332 again employs crusher gradients.

The negative prephasing gradient pulse 326 winds up phase such that, atthe signal echo following the second 180° RF pulse, signal acquisitionstarts at high k-value, which may then be subsequently decremented (orincremented or varied in orientation) for further acquisitions, as willbe described below. As energy density in the signal is generallyproportional to k⁻¹, this method ensures k-values with lower SNR areacquired first, before T₂ effects have caused much overall signalreduction.

With all gradients off, a receive gate 333 is opened to receive the RFsignal, which is shown in FIG. 3 as pulse 334 on trace 336. The RFsignal in trace 336 is a representation showing only the signal presentin the receive gate window without showing the actual details of the RFsignal outside the window. Sampling occurs as represented by trace 338beginning with the initial k-value, 340 a, seen on trace 324. Note that,at the scale of the drawing, the sampling rate is high enough that theindividual triggers of the analog to digital converter (A/D) have mergedtogether in trace 338. (The expanded time scale in FIG. 4 describedbelow shows the individual A/D triggers.)

In regions of k-space where the corresponding coefficients aresufficiently large that they can be well-estimated using a small subsetof the samples of one echo, acquisition of another k-value, obtained byapplying a gradient pulse 342 a shown on trace 314, to select a newk-value, during the time the echo is being recorded, is accomplished.After a suitable settling time, another set of samples of the echo (nowderived from the new k-value coefficient) can be collected. This processcan be repeated, acquiring multiple samples at each of a select set ofk-values within one TR. A plurality of samples are taken at the initialk-value 340 a. A k-value selection gradient pulse 342 a is then appliedand the resultant k-value 340 b is sampled. (Though shown in the figureas a negative pulse on the second gradient, decrementing the k-value, inpractice this pulse and subsequent k-value gradient pulses can bedesigned through any vector combination of gradients to select anyk-value or orientation.) Similarly, the k-value selection gradient pulse342 b, selects a third k-value 340 c which is sampled by the A/D. Eachgradient pulse changes the phase wrap, selecting a new k-value.Application of a k-value selection gradient pulse (342 c-342 f) followedby multiple sampling of the resultant k-value coefficient is repeated asmany times as desired. While data is being acquired throughout, thesamples of interest are acquired when all gradients are off. Thegradient orientations for slice and k-value select may be coincidentwith the machine gradients, which are aligned to lie coincident ororthogonal to the B₀ field. Alternatively, the acquisition directionsand k-value encodes may be selected using gradients that are a vectorcombination of all three machine-gradient axes.

In the circumstance where it is desired to measure a low SNR k-value theprewinding encoding gradient pulse can be set such that the firstk-value to be measured is the desired low SNR k-value. Alternatively,the prewinding gradient pulse can be set to zero so that the firstk-value measured is k0. A measurement of k0 may be desired for thepurpose of determining the systems receiver sensitivity to theparticular VOI, determining the relative prevalence of isochromats(e.g., water vs. lipids) irrespective of texture in the VOI, or for thepurpose of establishing a reference value for normalization of the otherk-values measured in a VOI or for comparison with k-values from otherVOI. Furthermore a strategy for gathering a specified set of k-valuesfor a VOI may include measuring the low SNR k-values (typically thehigher k-values) in a first set of multiple TR and then measuring k0 andother higher SNR k-values in other TRs while remaining in the same VOI.

As is shown diagrammatically in FIG. 3, the signal magnitude reaches amaximum at the time of the spin echo. It is also shown diagrammaticallythat the signal magnitude is varying throughout the acquisition of themultiple RF measurements of a k-value and more so between successiveblocks of measurements of k-values. Alignment in time of the measurementof the low SNR k-values with the highest echo signal enhances the SNR ofthe k-value measurement, alternatively alignment of higher SNR k-valueswith lower echo signal magnitude allows gathering additional usefulk-value acquisitions during the echo.

FIG. 4 shows a close-up of the pulse sequence of FIG. 3 during theinitial portion of the RF sampling window 338 between 7.25 and 8.00msec. Multiple samples of the same k-value, taken in rapid successionwith all gradients off, provide the input for signal averaging to reduceAWGN when SNR is low. In a first block 344 a of the sampling window 338,multiple samples 346 a are taken of the first k-value 340 a. Duringapplication of the k-value selection gradient pulse 342 a, transitionsamples 348 a are taken. When the k-value selection gradient is switchedoff, multiple samples 346 b are taken at the second k-value 340 b.Application of k-value selection gradient pulse 342 b then occurs withassociated transition samples 348 b, and subsequent acquisition ofsamples 346 c of the third k-value 340 c after the gradients areswitched off. The underlying signal is minimally impacted by motion dueto the very short time window used to acquire data at each givenk-value. Since the data is acquired with gradients off, there is noissue with chemical shift and the effective T₂* is longer, boosting thesignal value.

The sampled values of the echo, acquired while the k-value selectiongradient pulse is ramped up, held steady, and then ramped down to zero,will necessarily be influenced by the applied gradient. These transitionsamples may provide other interesting information, but are not used inthe consideration of a straight measurement of the k-value coefficient;only those samples which are recorded when there is no gradientcurrently active are used for this.

A consistent number of samples at each k-value can be acquired, or analternative sequence may be employed where, as k-values decrease, henceincreasing in signal amplitude, fewer samples are acquired. A pulsesequence designed for this type of acquisition is illustrated in FIG. 5.Multiple samples of each k-value targeted in the acquisition areacquired in rapid succession, with all gradients off. These repeatsprovide the input for signal averaging in low SNR signals. As with thepulse sequence, depicted in FIGS. 3 and 4, the underlying signal isminimally impacted by motion due to the very short time window in whichdata is acquired for a given k-value.

Samples within the portions of the sample window 344 a-344 g outlined onFIG. 5 correspond to the number of samples acquired for a given k-value340 a-340 g each induced by an unwinding pulse 342 a-342 f of thek-value selection gradient. N_(k), the number of samples associated witha given k-value, can be selected based upon expected SNR, tissuecontrast, contrast to noise, pathology, texture size, and/or texturebandwidth. For the example in FIG. 5 it can be seen that a decreasingnumber of samples are taken for progressively smaller k-values (largertextural features). This is because, as previously discussed, to firstorder signal amplitude increases with decreasing k-value—energy densityis generally proportional to k⁻¹. For this same reason, larger k-valuesare acquired first in this scheme, when T2 effects are least, the longerwavelength, higher signal strength, k-values being recorded later in theacquisition.

Refocusing the echo, and/or a new TR can be used to build up a libraryof k-space samples. Acquisition of multiple k-values within one TR canbe facilitated by application of multiple refocusing gradients and/or RFpulses, to increase the time over which the additional k-values can besampled within a TR. These later echoes would presumably be used toacquire the coefficients of the lower k-values in the selected set, astheir energy density in the continuum of values is generally higher sothe effect of T₂ decay on overall signal magnitude will not affect themas severely as it would the higher k-values. In this way a largerportion of the required k-space filling can be accomplished over fewerTRs, allowing more rapid data acquisition, minimizing the need forrepositioning the VOI.

FIG. 8 shows an extension of the basic sequence of the method claimedherein, using spin-echo refocusing to extend the record time for the TR.Application of a refocusing RF pulse 802 with an associated gradientpulse 804 results in slice-selective refocusing. After an appropriatesettling time, a second sampling window 806 is opened by the receivegate 808. Multiple k-value selection gradient pulses 810 are applied toincrement the selected k-value and, after switching off each successivegradient pulse, multiple samples of the selected k-value are acquired inthe sampling window. A second slice-selective refocusing RF pulse 812with associated gradient pulse 814 again inverts the spins and, afterapplication of each in the multiplicity of k-value selection gradientpulses 820, data is acquired in a third sampling window 816, opened bythe receive gate 818. As shown in the drawing, an increasing number ofk-values may be sampled with each refocusing. Refocusing can be repeateduntil the decrease in signal level from T2 and other effects makesfurther signal acquisition ineffective. Another method to extend therecord time by exciting multiple signal echoes, is to use one, or aseries of, gradient recalled echoes (GRE). GRE are different from the SEin that they cannot refocus the effects of stationary inhomogeneities,so T2* effects limit the number of repeats.

In addition to the tissue contrast available, the k-values associatedwith particular pathology will be part of the determination of thenumber of samples needed for signal averaging, N_(k). In liver fibrosis,the wavelength of pertinent textures is in the range of 400 microns,i.e. a k-value of 2.5 cycles/mm. This is similar to the texturalspacings seen in fibrotic development in many other diseases, such ascardiac fibrosis. The spacing of elements in trabecular bone varies alot, but the minimum spacing of interest is the width of trabecularelements, which are approximately 80 microns, setting a maximum k-valueof 12.5 cycles/mm. In neuropathology, many of the textures of interestare very fine, on the scale of 50 microns, equivalent to a k-value of 20cycles/mm

Each pathology will dictate what exactly is needed as quantitative data,i.e. what part of the continuum of k-values needs to be monitored, andwith what resolution and sensitivity. In some pathologies, short (long)wavelength features increase at the expense of long (short) wavelengthfeatures (e.g. liver fibrosis). In other pathologies, an amplitudedecrease and broadening of short wavelength features indicates diseaseprogression—e.g. degradation of the ordered formation of corticalneuronal minicolumns (approximately 80-micron spacing) with advancingdementia. In bone, with increasing age, first the highest k-valuefeatures disappear in the structural spectrum. Next the major structuralpeaks shift slowly towards lower k-values with advancing osteoporosis,the pace of this shift accelerating as an increasing percentage oftrabecular elements thin to the point that they break.

The signal level obtainable will depend on anatomy to some extent. Forinstance, though the resolution needed is highest in brain, theproximity of the cortex to the surface of the head ensures that use of asurface coil will provide significant signal boost for corticalstructures. Lower resolution is required in liver, as the structures ofinterest are on the order of several hundred, rather than tens ofmicrons. But, the organ is deeper (further from the coil) reducing themeasured signal. Using the in-table coil for spine data acquisitionyields modest signal level and good stabilization. Also, bone is a highcontrast target, so the SNR requirement is not as stringent. For allthese reasons, the exact number of repeats needed for averaging dependson more than the k-value range targeted.

FIG. 6 shows a simulation demonstrating that the ability provided by theclaimed method to acquire many repeats of a targeted k-value within asingle TR enables robust signal averaging to boost SNR. Assuming asubject displacement rate (which has in practice been measuredclinically over the course of several scans) of 30 μm/sec, and asampling rate=33.3 kHz (ΔTsample=30 μs), 90 repeat samples for averagingcan be taken rapidly enough that, even up to a k-value of 20 cycles/mm(texture wavelength=50 μm), the acquisition remains immune to motioneffects.

FIG. 7 shows that, for comparison, using the conventional approach ofacquisition of a spatially encoded echo, even assuming a relatively fastgradient refocus sequence, which would provide a sampling rate of about67 Hz (ΔTsample=15 msec), subject motion over the time needed for 90repeats would severely degrade the signal, and any ability to improveSNR by signal averaging. The situation is actually worse due to the factthat to acquire 90 repeats using conventional spatially-encoded echoeswould require several TRs, making the acquisition time significantlylonger, and the signal degradation due to motion much more severe. Withthe exception of the very lowest k-values, the potential SNR gain due tomultiple sample combination has been nullified by the effects of motion.

By acquisition of a large enough selected range of k-values,construction of a structural profile in one or more dimensions becomes apossibility. As discussed above, refocusing echoes within a single TR,or multiple TRs, can be used to build up a library of k-space samples.Phase coherence might not be maintained between different k-values ifthey are acquired in TRs separated temporally such that displacement hasoccurred between them. If our primary interest is in the relativestrength of signals at particular k-values, this is not a problem. Ifcreation of a profile or an image from this library of values isdesired, the necessary post processing will have as input the high SNRmeasure obtained within each TR using the method claimed herein. Thesemeasures can then provide robust input for any required reregistrationbetween echoes or TRs towards constructing a profile. As an exampleselection and measurement in a first TR of a set of selected k-valuesmay be accomplished with at least one having a low k-value. In asubsequent TR, selection of the same set of k-values will allowre-registration of the data between the two TRs since even ifsignificant motion has occurred the phase change in the low k-valuephase shift will be less than for higher k-value textures and may becorrelated between the two TRs. Basically, the higher the k-value, thegreater the phase shift due to subject motion. Acquiring signal fromsuccessive encodes with a large difference in k-value enables a betterestimate of phase shift by careful comparison of the apparent phaseshift for each.

This is very similar to x-ray diffraction, wherein the magnitude-onlyinformation (no phase) obtained presents the challenge of determining abest estimate of the corresponding structural profile based on thismagnitude-only information. Algorithms exist towards solving theproblem, the chance of success depending on the range of k-valuecoefficients obtained, the SNR of each averaged coefficient, and thewidth of values contained in a nominally single-valued acquisition ofk-value. The chance of success in this effort is greatly increased usingthe claimed method due to its immunity to subject motion.

The ability to reconstruct a profile from k-value data depends somewhaton the spectral broadness of each single-k-value acquisition. While thisis influenced by the VOI (Volume of Interest) size and shape, it is alsoinfluenced by k-value and pathology, as degradation of tissue oftentends to lead to more textural randomness within tissue.

Selection of the VOI—shape, dimensions, orientation, and positioningwithin an organ/anatomy affects the data measured and itsinterpretation. The VOI shape can be chosen to maximize usefulness ofthe acquired data. Data can be acquired in different directions, and atdifferent textural wavelengths (k-values) within a VOI enablingassessment of textural anisotropy. Texture can be sampled in multipleVOIs, either interleaved within a single TR, or in successive TRs,towards assessment of pathology variation across an organ. Standardinterleaving processes for the VOI may be used within a TR to provideadditional data by applying additional encoding pulses on vectorcombination gradients and associated k-value selection gradient pulsesfor k-values in the interleaved VOI. As previously described, additionalexcitation RF pulses with associated slice selection gradients may berepeated within the same TR by exciting a volume of interest with agradient set in each repeat having at least a first gradient with analternative orientation from the first gradient pulse 308 appliedinitially in the TR, to define an additional VOI for excitation in newtissue, that does not overlap any previous VOI in the TR (fourth, fifthand sixth gradients in a first repeat and succeeding incrementalgradients in subsequent repeats). This response can be mapped, or theseveral measures taken and averaged, whatever is appropriate for thetargeted pathology. This is similar to the multi-positioning of tissuebiopsy. However, in the case of tissue biopsy, the number of repeats islimited due to the highly invasive nature of the technique. The minimumnumber of structural oscillations to be sampled at a specific k-valuedictates a minimum VOI dimension in the direction of sampling—the lengthrequired varying inversely with targeted k-value.

To ensure adequate sampling of structure when targeting a range ofk-values, the VOI dimension in the sampled direction can be heldconstant for all k-values in the targeted range, with the result thatthe number of structural oscillations sampled will vary with k-value.This is a simple solution, requiring the sampling dimension be set bythe lowest k-value (longest wavelength structure). Using this approach,the sampling dimension of the VOI is larger than required for thehighest k-value in the range, thus providing less localization withinthe tissue than would be otherwise possible.

Alternatively, data at widely differing k-values can be acquired insuccessive TRs, using changing VOIs tailored to the specific k-valuetargeted. Or, the dimensions of the VOI can be selected such thatacquisition in different directions within the VOI will be tailored tosampling in a specific textural frequency (k-value) range.

Similarly, the VOI may be held constant and the vector combinationgradient for the encoding and k-selection pulses may be altered from TRto TR for assessing feature size.

In some instances, it is desired to localize tightly in the spatialdomain, to broaden the localization in k-space. By defining a non-cubicacquisition volume, it would be possible to acquire data from differingk-values along the different (orthogonal or other) directions within theVOI, within one TR. The elliptical cross-section VOI 902 in FIG. 9B isone such possibility. Acquisition along any radial direction, as well asalong the axis of the shape, would be possible within one TR.

Additionally, one could use the flexibility of the method claimed hereinto sample k-space in a linear or in a curved trajectory. For example,texture could be sampled along radial lines, or along an arc or aspiral, to extract information of textural sizes along different spatialdirections. These methods can be used to determine the anisotropy oftexture, or the sensitivity to alignment in structures that aresemi-crystalline, such as cortical neuronal columns, or to more rapidlybuild up a library of k-values within a targeted extent of tissue in anorgan.

During one TR (i.e., one 90 degree excitation) k-value encodes can beapplied in multiple directions by changing the applied vectorcombination gradients for encoding and k-selection pulsing. The exactform of the VOI and sampling direction can be used to yield muchtextural information. For instance, the organization of cortical neuronfiber bundles is semi-crystalline, as the bundles in healthy tissue formin columns. Because of this, the measure of textural spacingperpendicular to the bundles is very sensitive to orientation. When theorientation is exactly normal to the columns, a very sharp signalmaximum is expected, the signal falling off rapidly as the orientationvaries on in either rotational direction away from this maximum. One wayto measure the spacing and organizational integrity (a marker ofpathology) would be to “rock” the acquisition axis around this maximumlooking for a resonance in signal intensity. This approach of lookingfor “textural resonances” by looking for signal maxima can be applied inany tissue region. As pathology degrades the organizational integrity,the sharpness of this peak will degrade and the signal maximum will bereduced.

Similarly, the randomness of the spacing in certain textures can beassessed by varying the length of tissue sampled in a specific, or inmultiple directions, with subsequent change in acquisition length. Theselected value for that length can be varied over multiple TRs to testthe sensitivity of the measured coefficient to this parameter.

The VOI can be selectively excited by a number of methods, for instanceintersecting slice-selective refocusing, selective excitation usingphased-array transmit in combination with appropriate gradients,adiabatic pulse excitation to scramble signal from the tissue outsidethe region of interest, as examples. Parameter selection for the variousmethods can be done with SNR optimization in mind. For instance, the VOIgenerated by a slice selective excitation and two additionalmutually-orthogonal slice selective refocusing pulses, as by VOI 904 inFIG. 9A. Through careful RF pulse design, the shape of the VOI can bedesigned so that the edges are smooth and more approximate a windowingfunction, as shown in FIG. 9B. These windowing functions provide thevolume selection without adverse impact on the spatial frequencies.Recall, in Fourier theory, each spectral line is smeared by theconvolution of a Fourier transform of the window function. We want tominimize this smearing of the underlying spectrum, as it decreases theenergy spectral density, and adversely impacts SNR.

Importantly, as has been discussed previously, the VOI can be moved fromplace to place within an organ or anatomy under study to measure thevariation of texture/pathology. This response can be mapped, or theseveral measures taken and averaged, as appropriate for the targetedpathology. This is similar to the multi-positioning of tissue biopsy.However, in the case of tissue biopsy, the number of repeats is limiteddue to the highly invasive nature of the technique.

Different diseases and conditions affect tissue in different ways.Generally, pathology advancement entails: 1) a loss of energy density inspecific regions of k-space, and/or 2) a shift in textural energydensity from one part of k-space to another, both effects beingaccompanied by 3) changes in the width of existing peaks in thecontinuum of textural k-values. Using trabecular structure as anexample—with decreasing bone health the average separation of trabecularelements widens (texture shifts to lower k-values) and becomes moreamorphous (broader peaks in k-space), while in parallel the structuralelements thin (a shift to higher k-values in a different part of thespectrum). Other tissues/organs are affected by diseases that have theirown individual signatures in tissue texture.

Using the method claimed herein, k-space is probed to reveal texture insuch a way as to eliminate the loss of signal resolution that resultsfrom subject motion blurring. Instead of measuring the large continuumof k-values needed to create an image, the focus here is on acquisitionof a select few k-values per TR, with sufficient repeats of each toyield high SNR. Each of the individual acquisitions is centered on asingle k-value. While the spatial encode is, to first order, a singlespatial frequency sinusoidal encode, there are a number of factors whichhave the effect of broadening the spatial frequency selectivity of thek-value measurement. One significant factor affecting the broadness, orbandwidth, of the k-value measurement is the length of the sampledtissue region. A longer sampling length encompasses more texturalwavelengths along the sampled direction, which has the effect ofnarrowing the bandwidth of the k-value measurement. (This is the inverserelationship between extent of a measurement in real and in k-space.)Hence, an aspect of the claimed method is the ability to set thebandwidth of the k-value measurements by appropriate selection of thesampling length within the VOI. Using this method, the bandwidth of themeasurement can be set according to the desired k-space resolutionappropriate to the tissue being evaluated. (Need both high k-values forgood texture resolution, and high resolution in k-space for sensitivemonitoring of pathology-induced changes.) For highly ordered structuresone could choose a set of narrow bandwidth measurements distributed overthe expected range of texture wavelengths, whereas in a more randomlyordered structure, such as the development of fibrotic texture in liverdisease, one could choose to use a single, or a few, broadband k-valuemeasurements to monitor development of the fibrotic texture.

A measure of both the relative intensities of the various texturalk-values in a tissue and the broadness of the peaks along the continuumof textural k-values present within the texture under study is needed.As such, data acquisition can be designed to probe specific region(s) ofk-space, with parameter selection that will enable measurement of therelative width of peaks arising from the underlying tissue, rather thanthat resulting from experiment parameters. It is necessary to recognizethe interaction of the two components, and design experiments to yieldthe best measure of pathology-induced tissue changes.

Keeping in mind the inverse relationship between localization in realand in k-space, and using the example of bone again: Trabecular elementsare on the order of 80 microns in width, hence a large range offinely-sampled k-values is required to monitor their thinning. Toachieve fine sampling of each k-value (good localization in k-space) along sampled region in tissue space would be required.

The ability to tailor the width of the peak in k-space by varying thelength of tissue sampled suggests various approaches, all possible whilemeeting the requirement for rapid acquisition of repeat measures ofindividual k-values to ensure motion immunity. For instance, the generaldistribution of textural frequencies present in the tissue can bemeasured using broad, overlapping peaks in k-space. Then VOI dimensionscan be varied to allow greater resolution in k-space to determine, withhigh sensitivity, the distribution of k-values/textural frequenciespresent in the tissue.

An additional feature of the method claimed herein is that, whileenabling the motion immunity needed for robust assessment of pathology,contrast can also be improved through application of the claimed methodin conjunction with appropriately selected and implemented contrastmechanisms. The method is such that combination with many pertinentcontrast mechanisms is easily accomplished.

Though a good SNR level is needed, another very important figure in MRimaging is contrast to noise (C/N). A large signal is of little benefitif there is no contrast to highlight the structures of interest againstthe surrounding tissue, whether it be negative or positive contrast. Asdiscussed above, the magnetic resonance modality allows tuning ofcontrast to highlight tissue structures/morphology under study. Forinstance, T1 contrast can be used to highlight fat, T2* providescontrast to the hemoglobin in blood, T2 contrast can be used tohighlight water containing tissue/sera. A range of other contrastmechanisms have been developed for this modality, such as BloodOxygenation Level Dependent (BOLD) contrast, Dynamic SusceptibilityContrast (DSC) imaging, and Arterial Spin Labeling (ASL) to name a few.

In these contrast schemes blood, labeled either magnetically or throughuse of a contrast agent, flows through the anatomical region understudy. Rapid image acquisition is done over the time period during whichthis bolus of labeled blood courses through the targeted brainarea/vasculature or changes with time due to local metabolic or otherprocesses. By incorporating the claimed method into these schemes,changes in texture vs. time can be determined with high spatial andtemporal resolution.

Additionally, diagnostic boundaries in many diseases are being extendedthrough combination of multiple imaging methods. Use of both differingtechniques within a single modality—structural MR, functional MR, DWI(Diffusion Weighted Imaging), DTI (Diffusion Tensor)-MRI—and differingmodalities—MR combined with PET (Positron Emission Tomography), CT(Computed Tomography), etc.—yields more information than any onetechnique could on its own. In this way, traction is being gained inearly diagnosis of a range of diseases/pathologies. Advancement indiagnostic science is often from a new technique that can, along withproviding valuable diagnostic information pertinent to a particularpathology in its own right, complement current and emerging practice.

As an example, diagnosis and monitoring of ischemic stroke has beengreatly facilitated by combining DWI-MRI with standard structural MRimaging. Current studies link MR functional contrast from BloodOxygenation Level Dependent (BOLD) imaging with both structural MRimages and DWI-MRI images. Structural MR image segmentation techniquesare used to assess cortical tissue degradation; this information is thencombined with both glucose metabolism and amyloid plaque depositioninformation derived from PET studies. PET is combined with anatomicalMRI and with CT in cancer diagnosis.

The increasing range of techniques/contrast mechanisms available in MRIenables combining different diagnostic measurements/techniques withinone exam, facilitating data comparison.

DWI, a measure of the voxel-averaged rate of diffusion of watermolecules in the target tissue, has increased in use significantly inrecent years, as has the derivative technique DTI which assesses thedirectional anisotropy in the diffusion process. The main clinicaldomain of application of diffusion imaging is neurologic disorders,especially for management of patients with acute stroke. Additionally,in conjunction with structural MRI, diffusion imaging is routinely usedto diagnose Creutzfeld-Jakob Disease (CJD), diffuse axonal injury,evaluate white matter damage in MS, aid in the diagnosis of tumormetastases (breast, prostate, liver), and evaluate tissue changesassociated with dementia onset and as an in vivo measure of braincircuitry.

Diffusion imaging provides quantitative information dependent on thevoxel-averaged microscopic properties of tissues, such as cell size andshape, packing, etc. through the observation of the diffusion of watermolecules. However, many theoretical and experimental analyses of theeffect of microscopic tissue properties on diffusion highlight thedifficulty in properly interpreting diffusion data to accurately inferthe microstructure and microdynamics of biological systems.

Although DWI/DTI is a quantitative measure, it is not a direct measureof these microscopic properties, but is an average over a voxel ofseveral cubic mm. In order to interpret changes in the Average DiffusionCoefficient (ADC) within the voxel, certain assumptions are made, suchas tissue homogeneity and type of structure causing the diffusionvariation. The exact mechanisms governing water diffusion processes intissues, especially in the brain, are not clearly understood. What isinferred from the measurement regarding the underlying barriers andrestrictions to free diffusion is based on certain assumptions.(ALEXANDER, A., et al.; “Diffusion Tensor Imaging of the Brian”;Neurotherapeutics 4(3), 2007. LE BIHAN, D., JOHANSEN-BERG, H.;“Diffusion MRI at 25: Exploring Brain Tissue Structure and Function”;Neuroimage, Vol. 61, 2012.) Additional information on these underlyingstructures could help clarify the proper form of these assumptions,adding to the diagnostic accuracy.

The diffusion signal is low and is extremely sensitive to subjectmotion. Long and high gradients are required to create the diffusionweighted signal, resulting in a long TE, with significant T2 and T2*decay during signal readout, hence low SNR. The strength and duration ofthe gradients result in high motion sensitivity, as any motion in thegradient direction during the time of gradient application will looklike diffusion. (LE BIHAN, D., JOHANSEN-BERG, H.; “Diffusion MRI at 25:Exploring Brain Tissue Structure and Function”; Neuroimage, Vol. 61,2012.) The signal is sensitive to diffusion time—a long enough diffusiontime that water molecules have time to interact with the tissuestructure is required. In most clinical situations, diffusion timesrange between 30 and 50 ms which translates to about 13-17 μm. Anysubject motion over that time appears as diffusion on the signal.Further the fast rise/fall time of the gradients results in eddycurrents that lead to image misregistration decreasing the SNR. (“AHitchhiker's Guide to Diffusion Tensor Imaging”; SOARES, J. M., et al.;Frontiers in Neuroscience, 12 Mar. 2013. ALEXANDER, A., et al.;“Diffusion Tensor Imaging of the Brian”; Neurotherapeutics 4(3), 2007.MASCALCHI, M., et al.; “Diffusion-weighted MR of the brain: methodologyand clinical application”; Radiologia Medica, 109(3) 2005.)

In contrast to DWI and DTI, the method claimed herein offers a directmeasure of very fine (near cellular) texture. As such, it providesvaluable complementary diagnostic information as it measures very finestructure, and DWI/DTI measure the effects of that structure ondiffusion. Along with the ability of the method claimed herein toprovide intricate textural information towards pathology assessment,when used at the front-end of an MR exam that includes DTI it canprovide information on the structural boundaries/hindrances in anydirection, clarifying interpretation of the DTI signal. Additionally,the method claimed herein enables robust measure of very small scaledisplacements—it could provide a measure of motion occurring over thecourse of the DTI acquisition, towards post processing reregistration.The different sequences could, perhaps, be intermingled in time tooptimize the effect. Some possible targets for combining the methodclaimed herein with DTI would be in assessing vasculature in tumors,measuring changes in gray matter with dementia onset. (WESTON, P., etal.; “Diffusion imaging changes in grey matter in Alzheimer's disease: apotential marker of early neurodegeneration”; Alzheimer's Research andTherapy, 7: 47, 2015.), assessing hyperplasiac development of mammaryducting in breast cancer progression, and diagnosis and monitoring ofMS, to name a few. The method claimed herein can be applied in tissueregions showing abnormalities in DWI, to measure textural variationsindicative of pathology. Measurement of the variation in texture as afunction of direction can be further used as an input to guideapplication of DTI, for correlation with and verification of the thefractional anisotropy measure of DTI.

Arterial Spin Labeling (ASL) is another MR-based technique, whichprovides a non-invasive measure of cerebral blood flow (CBF) bymagnetically labeling blood prior to its movement into the image plane,and then subtracting a background (unlabeled) image. The correspondencebetween perfusion and metabolism make the technique similar tofunctional imaging. Research is ongoing into application of thetechnique to a range of pathologies, including cerebrovascular disease(CVD), dementia, differentiating tumor recurrence from radiationnecrosis, and epilepsy. (DETRE, J., et al.; “Applications of ArterialSpin Labeled MRI in the Brain”; Journal of Magnetic Resonance Imaging,35(5), 2012. PETCHARUNPAISAN, S., et al.; “Arterial spin labeling inneuroimaging”; World Journal of Radiology, 2(10), 2010.)

SNR is low in ASL because the signal from the magnetically labeled waterin blood is only about 0.5% to 1.5% of the full tissue signal. A majorsource of error in perfusion quantification is the arterial transit timefrom the tagging region to the imaging slice.8 In healthy subjects thisis about 1 sec, while it is longer in patients with occluded vessels. Asboth a background and labeled image must be acquired, subject motion isa serious issue. Also, the 5-10-miute acquisition time makes ASL asomewhat long add-on to an already scheduled clinical exam. Fastacquisition techniques, such as EPI, help somewhat here, but subjectmotion and low SNR remain a problem. (DETRE, J., et al.; “Applicationsof Arterial Spin Labeled MRI in the Brain”; Journal of MagneticResonance Imaging, 35(5), 2012.) The method claimed herein could provideinformation on vasculature pathology when used as a complementarytechnique to ASL-acquired data or in conjunction with ASL contrast. Inthe latter embodiment, the method claimed herein would use themagnetically labelled blood to heighten contrast, facilitatingmeasurement of changes in vessel texture (vessel density and spatialrandomness) as an indicator of pathology such as CVD, tumor, or thevasculature changes attendant with dementia progression.

Having now described various embodiments of the invention in detail asrequired by the patent statutes, those skilled in the art will recognizemodifications and substitutions to the specific embodiments disclosedherein. Such modifications are within the scope and intent of thepresent invention as defined in the following claims.

What is claimed is:
 1. A method for selective sampling to assess textureusing magnetic resonance (MR) comprising: exciting a volume of interest(VOI) employing a plurality of gradients; encoding a specific k-valuewith a selected gradient pulse; turning off all gradients; and,recording multiple samples of an RF signal encoded with the specifick-value in a single TR.
 2. The method as defined in claim 1 wherein thesteps of exciting a volume of interest and encoding comprise:transmitting a first RF pulse with a first gradient chosen for firstslice selection; transmitting a second RF pulse with application of asecond gradient chosen for slice selective refocusing in a regiondefined by an intersection of the first slice and a second slice;applying an encoding gradient pulse to induce phase wrap to create aspatial encode for the specific k-value and orientation; transmitting athird RF pulse with a third gradient activated, said third gradientadapted for slice selective refocusing, defining a region defined by theintersection of the first and second slices and a third slice selectionto define the VOI.
 3. The method as defined in claim 2 furthercomprising: issuing a k-value selection pulse on a selected vectorcombination gradient to determine a second k-value; turning off thevector combination gradient; and, recording multiple samples of the RFsignal at the second k-value in the TR.
 4. The method as defined inclaim 2 further comprising: applying additional k-value selection pulsesfor a predetermined plurality of pulses on a selected vector combinationgradient, each k value selection pulse determining a next k-value;turning off the vector combination gradient after each pulse; and,recording multiple samples of the RF signal at the next k-valuedetermined by each k value selection pulse in the TR.
 5. The method asdefined in claim 4 further comprising: transmitting a refocusing RFpulse; issuing a plurality of k-value selection pulses on the vectorcombination gradient, each pulse determining to a new k-value; turningoff the vector combination gradient after each pulse; and, recordingmultiple samples of the RF signal at the next k-value determined by ateach k valued selection pulse in the TR.
 6. The method as defined inclaim 5 further comprising: transmitting a second refocusing RF pulse;issuing a plurality of k-value selection pulses on the vectorcombination gradient, each pulse determining a new k-value; turning offthe vector combination gradient after each pulse; and, receivingmultiple samples of the RF signal at the next k-value determined by ateach k value selection pulse within the TR.
 7. The method as defined inclaim 4 further comprising: applying a refocusing gradient on one of thefirst, second or third gradients; issuing a plurality of k-valueselection pulses on the vector combination gradient, each pulsedetermining a new k-value; turning off the vector combination gradientafter each pulse; and, receiving multiple samples of the RF signal atthe next k-value determined by at each k value selection pulse withinthe TR.
 8. The method as defined in claim 4 wherein the number ofsamples at each new k-value following each of the plurality of k-valueselection pulses on the vector combination gradient is selected basedupon expected SNR, pathology, tissue contrast, texture size or texturebandwidth.
 9. The method as defined in claim 1 wherein the volume ofinterest in the step of exciting a volume of interest is selected usingthe set of intersecting slice-selective refocusing, selective excitationusing phased-array transmit in combination with appropriate gradients,adiabatic pulse excitation to scramble signal from the tissue outside aregion of interest outer volume suppression sequences, and other methodsof selectively exciting spins in an internal volume including physicallyisolating the tissue of interest.
 10. The method as defined in claim 1wherein the steps of exciting a volume of interest, encoding a specifick-value, turning off all gradients and recording samples of an RF Signalat the specific k-value are repeated multiple times in succeeding TR tobuild a magnitude power spectrum, the specific k-values in each TRindependent of intervening motion if constrained within the volume ofinterest for each TR.
 11. The method as defined in claim 1 wherein thesteps of exciting a volume of interest, encoding a specific k-value,turning off all gradients and recording samples of an RF Signal at thespecific k-value are repeated multiple times in succeeding TRs withvariation of the volume of interest thereby acquiring textural data toassess a textural variation within or across an organ or anatomy. 12.The method as defined in claim 1 wherein the steps of exciting a volumeof interest, encoding a specific k-value, turning off all gradients andrecording samples of an RF Signal at the specific k-value are repeatedmultiple times in succeeding TRs with alteration of a selected vectorcombination gradient for the selected gradient pulse to assess featuresize.
 13. The method as defined in claim 2 wherein the steps of excitinga volume of interest, encoding a specific k-value, turning off allgradients and recording samples of an RF signal at the specific k-valueare repeated multiple times within the TR, wherein the step of excitinga volume of interest is accomplished with a gradient set in each repeathaving at least a first gradient with an alternative orientation todefine an additional VOI for excitation in new tissue, that does notoverlap any previous VOI in the TR.
 14. The method as defined in claim 2wherein the encoding gradient pulse is set to zero.
 15. The method asdefined in claim 4 further comprising: selecting combinations of thefirst, second and next k-values obtained in the TR; reregisteringselection of the volume of interest based on the selected combinationsfor a subsequent TRs.
 16. The method of claim 3 wherein the specifick-value and the second k-value are chosen with at least one of thespecific k-value and second k-value being low and further comprising: ina second TR encoding the specific k-value with an encoding gradientpulse; turning off all gradients; recording a second set of multiplesamples of an RF signal encoded with the specific k-value; issuing ak-value selection pulse on the selected vector combination gradient forthe second k-value; turning off the vector combination gradient; and,recording a second set of multiple samples of the RF signal at thesecond k-value; and, re-registering the recorded multiple samples of theRF signal at the specific k-value and the second set of recordedmultiple samples of the RF signal at the specific k-value and therecorded multiple samples of the RF signal at the second k-value and thesecond set of recorded multiple samples of the RF signal at the secondk-value based on correlation of data for the low k-value.
 17. The methodas defined in claim 1 further comprising selecting a narrow bandwidth toallow single species signal acquisition for enhanced k-value.
 18. Amethod for selective sampling to assess texture using magnetic resonance(MR) comprising: transmitting a first RF pulse with a first gradientchosen for first slice selection; transmitting a second RF pulse withapplication of a second gradient chosen for slice selective refocusingin a region defined by an intersection of the first slice and a secondslice; applying an encoding gradient pulse to induce phase wrap tocreate a spatial encode for a specific k-value and orientation;transmitting a third RF pulse with a third gradient activated, saidthird gradient adapted for slice selective refocusing, defining a regiondefined by the intersection of the first and second slices and a thirdslice selection to define a volume of interest (VOI); turning off allgradients; and, recording multiple samples of an RF signal encoded withthe specific k-value; issuing a k-value selection pulse on a selectedvector combination gradient to determine a second k-value; turning offthe vector combination gradient; and, recording multiple samples of theRF signal at the second k-value in a single TR.
 19. The method asdefined in claim 17 further comprising: transmitting a fourth RF pulsewith a fourth gradient chosen for fourth slice selection; transmitting afifth RF pulse with application of a fifth gradient chosen for sliceselective refocusing in a region defined by an intersection of the firstslice and a second slice; applying a second encoding gradient pulse toinduce phase wrap to create a spatial encode for an additional specifick-value and orientation; transmitting a sixth RF pulse with a sixthgradient activated, said sixth gradient adapted for slice selectiverefocusing, defining a region defined by the intersection of the thefourth and fifth slices and a sixth slice selection to define a secondseparate VOI within the single TR; turning off all gradients; and,recording multiple samples of an RF signal encoded with the additionalspecific k-value; issuing a k-value selection pulse on a selected vectorcombination gradient to determine a second additional k-value; turningoff the vector combination gradient; and, recording multiple samples ofthe RF signal at the second additional k-value in the single TR.
 20. Themethod as defined in claim 18 wherein the encoding gradient pulse is setto zero.